[R-SIG-Finance] estimation difference between ugarchroll and ugarchfit‏

alexios ghalanos alexios at 4dscape.com
Wed May 23 22:05:55 CEST 2012


Hi Guoshi,

1.
#################################
# part of the code is straight out of the 'rugarch-rolling.R' file
# which I suggest you take a closer look at.
forecast.length = 400
refit.every = 1
n.ahead = 1
# your data size
T = 750
fwindex = t(rugarch:::.embed((T - forecast.length - n.ahead + 2):T, 
refit.every, by = refit.every, TRUE ) )
fitindx.start = c(fwindex[1,]-1)
fwindex.end = fwindex[refit.every,]
nf = length(fwindex.end)
forecast.length = floor(forecast.length/refit.every) * refit.every
####################################

# Do 10 fits to convince yourself it is the same:

lik = rep(0, 10)
for(i in 1:10){
dpx =  matrix(dp[(i*refit.every):(fitindx.start[i]+refit.every)], ncol=1)
spec = ugarchspec(variance.model = list(model = "eGARCH", garchOrder = 
c(1,1),
external.regressors =dpx ),
mean.model = list(armaOrder=c(0,0), include.mean = T, 
external.regressors = dpx), distribution.model = "norm");
myfit = ugarchfit(spec, 
logexr[(i*refit.every):(fitindx.start[i]+refit.every)], out.sample = 
refit.every, solver.control=list())
lik[i] = likelihood(myfit)
}

# use your the 'uGARCHroll' object to compare:
all.equal(lik, test at roll$LLH[1:10])
[1] TRUE


2. Correct. The rolling method does not take into account the presence 
of external regressors...most likely an omission on my part which I'll 
look to rectify during the next release. Do keep in mind that the 
ugarchroll method is just a convenience wrapper for fitting/forecasting 
with the existing methods.


-Alexios



On 23/05/2012 19:54, linpack wrote:
> Hi there,
>
> I have two questions (which might be related).
> I am trying to predict mean and variance of monthly stock excess return
> using dividend price ratio with a eGarch model.
>
> Data format is the following
> column 1 column 2 column 3
> gross S&P return gross t-bill return lagged dividend price ratio
> #data dimension is nrow=750 and ncolumns =3 and the "redp.dat" file is
> attached.
>
> code is following
> library(rugarch);
> dat=read.csv('C:\\redp.dat',header=F);
>
> logexr=log(dat$V1)-log(dat$V2); # log of gross excess return
> as.matrix(logexr);
> dp=log(dat$V3); #log of dividend price ratio
> as.matrix(dp);
>
> spec = ugarchspec(variance.model = li! st(model = "eGARCH", garchOrder =
> c(1,1), external.regressors = as.matrix(dp)),
> mean.model = list(armaOrder=c(0,0), include.mean = T,
> external.regressors = as.matrix(dp)), distribution.model = "norm");
> # dividend price ratio are used as external regressor in both mean and
> variance equation
>
> test=ugarchroll(spec, n.ahead=1, data=logexr, forecast.length=400,
> refit.every=1, refit.window="moving");
> as.data.frame(test, which='LLH')
> # where I did a rolling window forecast and display all the likelyhood
>
> #then I did a double check on the rolling estimation by doing
> for (j in 1:10) # I am justing printing the first ten loop step
> myfit = ugarchfit(spec, logexr[j:349+j]);
> likelihood(myfit)
> end
>
> # Question 1: the first likelihood from ugarchfit exactly equals that in
> ugarchroll, while the values are different from then on.
> # When I modify the spec to exclude any external regressor, the two sets
> of likelihood are then exactly the same.
> # reducing the forecast length (and therefore increase the length of the
> estimation window) seems to mitigate the estimation difference.
>
> # Question 2: I then compare the density forecast
> as.data.frame(test, which='density')
> # this seem to produce
> for (j in 1:10) # I am justing printing the first ten loop step
> ugarchforecast(myfit, n.ahead=1); # and again when the likelihood are
> different, the forecast is different.
> end
> # however, I am a bit confused since I thought it should produce
> for (j in 1:10)
> ugarchforecast(myfit, n.ahead=1,external.forecasts = list(mregfor =
> dp[350+j], vregfor = dp[350+j] ));
> end
>
> looking forward to your help.
>
>
> Guoshi
>
>
>
>
>
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